A new procedure to optimize the selection of groups in a classification tree: Applications for ecological data

Agglomerative cluster analyses encompass many techniques, which have been widely used in various fields of science. In biology, and specifically ecology, datasets are generally highly variable and may contain outliers, which increase the difficulty to identify the number of clusters. Here we present...

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Vydáno v:Ecological modelling Ročník 220; číslo 4; s. 451 - 461
Hlavní autoři: Guidi, Lionel, Ibanez, Frédéric, Calcagno, Vincent, Beaugrand, Grégory
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 24.02.2009
Elsevier
Témata:
Animal, plant and microbial ecology
Biological and medical sciences
Classification
Clustering
Copepoda
Dendrogram
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Life Sciences
Methods and techniques (sampling, tagging, trapping, modelling...)
Outliers
Stopping rule
Outliers
Stopping rule
Clustering
Dendrogram
Classification
Group selection
Ecology
Models
classification
stopping rule
outliers
ISSN:0304-3800, 1872-7026
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Shrnutí:Agglomerative cluster analyses encompass many techniques, which have been widely used in various fields of science. In biology, and specifically ecology, datasets are generally highly variable and may contain outliers, which increase the difficulty to identify the number of clusters. Here we present a new criterion to determine statistically the optimal level of partition in a classification tree. The criterion robustness is tested against perturbated data (outliers) using an observation or variable with values randomly generated. The technique, called Random Simulation Test (RST), is tested on (1) the well-known Iris dataset [Fisher, R.A., 1936. The use of multiple measurements in taxonomic problems. Ann. Eugenic. 7, 179–188], (2) simulated data with predetermined numbers of clusters following Milligan and Cooper [Milligan, G.W., Cooper, M.C., 1985. An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159–179] and finally (3) is applied on real copepod communities data previously analyzed in Beaugrand et al. [Beaugrand, G., Ibanez, F., Lindley, J.A., Reid, P.C., 2002. Diversity of calanoid copepods in the North Atlantic and adjacent seas: species associations and biogeography. Mar. Ecol. Prog. Ser. 232, 179–195]. The technique is compared to several standard techniques. RST performed generally better than existing algorithms on simulated data and proved to be especially efficient with highly variable datasets.
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ISSN:0304-3800
1872-7026
DOI:10.1016/j.ecolmodel.2008.11.006